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Change detection (CD) in high-resolution remote sensing images (RSIs) can be regarded as a binary visual recognition problem. Metric learning (ML) is a reliable method to determine pixel class attributes based on discriminative distance function between learnable image features. However, most related works learn discriminative distance functions in single-scale feature pairs, which suffer from slow convergence and poor local optima, partially due to that the loss function employs only large-scale feature samples while not interacting with the other scale features. Furthermore, more effective features are a prerequisite for improving the performance of ML-based CD. Hence, we propose a novel hierarchical metric learning network (HMLNet) with dual attention for CD in RSIs, where the key point is that hierarchical metric learning is performed in an ensemble manner to improve detection accuracy and accelerate model convergence. Specifically, based on the features extracted from the encoder-decoder backbone, we construct a feature pyramid to handle the complex details of objects at various scales in RSIs, and then perform metric learning between the paired pyramid features at the same scale. In addition, the dual-attention module is proposed to enhance the internal consistency of changed objects and effectively obtain more detailed information by acting on multi-scale pyramid features. Extensive experiments on the two public RSIs CD datasets, and the results demonstrate that the proposed HMLNet can accurately locate changed objects, which consistently outperforms the state-of-the-art CD competitors. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) Ship Detection (SSD) is an important application, and it has been widely used in commercial and military fields. With the development of remote sensing technology, the use of a large amount of increasing unlabeled data is a challenge for model training, and the interference caused by the reefs and the nearshore facilities is also a challenge for SAR ship detection. In this work, we propose a Feature Integration Attention Detection (FIAD) Network to detect ships in SAR images, namely the FIAD Net. The FIAD Net has two contributions: a ModiBYOL method based on self-supervised learning and a novel Feature Integration Attention (FIA) module. The FIA module could enhance the feature learning ability of the backbone network, and the ModiBYOL could utilize the unlabeled data as a source for backbone pre-training. Experiments on the OpenSARShip dataset, the SAR ship detection dataset (SSDD), and the SAR Ship dataset show that the proposed method achieves an accuracy comparable to the state-of-art methods with a speed of 47 Frames Per Second (FPS). Besides, the effectiveness of the proposed modules is studied through an ablation study. [ABSTRACT FROM AUTHOR]

The image labeling task of remote sensing image scene classification (RSSC) is based on the semantic content of remote sensing images. The semantic information within remote sensing photographs has become more complicated and difficult to detect as remote sensing technology has progressed. As a result, extracting more important semantic elements could aid in the completion of the RSSC assignment. Thus, in this research, we offer MLFC-Net, a multi-level semantic feature clustering attention model based on deep convolution neural networks (DCNNs) that extracts more accurate feature information. The concept of MLFC-Net stems from the utilization of rich spatial information found in remote sensing photos, but few approaches in the RSSC application considered merging general semantic feature information with clustered semantic feature information. By rearranging the weight of corresponding information, such as feature maps and tensor blocks of the feature map, we implemented the attention mechanism. To build a model with minimal computational cost and good portability, we use a channel-wise attention mechanism and an ensemble structure. We were able to improve the representation of several critical semantic aspects using the MLFC model. In the EuroSAT, UCM, and NWPU-RESISC45 RSSC datasets, the MLFC model's performance is demonstrated. And, on average, the MLFC model enhanced accuracy by 2.56 percent, 1.25 percent and 2.00 percent, respectively, producing results that were equivalent to the state-of-the-art. • A novel attention module is proposed for remote sensing scene classification. • The mechanism relies on extracting features from different levels. • The proposed method achieved competitive performance. • The proposed method implemented a small computational complexity. • The proposed method is with high portability. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) is an indicator for monitoring the trophic status of coastal water. However, its sampling and analysing in situ can be time consuming and spatially limited. With high temporal resolution and global coverage, Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 products are reliable and widely used for ocean ecosystem monitoring. The Landsat images can provide detailed spatial information for regional scale land cover and land change studies. The purpose of this study is to combine the 4 km resolution MODIS Chl-a data with 30 m resolution Landsat data to obtain high spatial resolution Chl-a maps for complex coastal water quality monitoring. A data fusion model of MODIS Chl-a and Landsat 8 OLI sensor data was built up for the east coast of the Laizhou Bay, China. The model is an optimal regression model of the band ratio OLI+2/OLI+5 and MODIS Chl-a. With residual correction, it managed to downscale the MODIS Chl-a data from resolution 4 km to 30 m. Validation were provided from in situ Chl-a measured in spring and autumn of 2013 and 2014. The model yields satisfactory results and the derived Chl-a map presents detailed information of coastal trophic status, which will be useful for regional water quality monitoring. [ABSTRACT FROM AUTHOR]